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Record W4379376261 · doi:10.15252/embr.202256678

Arms race between anti‐silencing and RdDM in noncoding regions of transposable elements

2023· article· en· W4379376261 on OpenAlexfundno aff
Taku Sasaki, Kae Kato, Aoi Hosaka, Yu Fu, Atsushi Toyoda, Asao Fujiyama, Yoshiaki Tarutani, Tetsuji Kakutani

Bibliographic record

VenueEMBO Reports · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyInstitute of GeneticsJapan Society for the Promotion of ScienceNational Institute of GeneticsHuman Frontier Science Program
KeywordsBiologyDNA methylationTransposable elementEpigeneticsGene silencingChromatinGeneticsHistoneArabidopsisGenomeDNAGeneGene expression

Abstract

fetched live from OpenAlex

Transposable elements (TEs) are among the most dynamic parts of genomes. Since TEs are potentially deleterious, eukaryotes silence them through epigenetic mechanisms such as repressive histone modifications and DNA methylation. We previously reported that Arabidopsis TEs, called VANDALs, counteract epigenetic silencing through a group of sequence-specific anti-silencing proteins, VANCs. VANC proteins bind to noncoding regions of specific VANDAL copies and induce loss of silent chromatin marks. The VANC-target regions form tandem repeats, which diverge rapidly. Sequence-specific anti-silencing allows these TEs to proliferate with minimum host damage. Here, we show that RNA-directed DNA methylation (RdDM) efficiently targets noncoding regions of VANDAL TEs to silence them de novo. Thus, escape from RdDM could be a primary event leading to the rapid evolution and diversification of sequence-specific anti-silencing systems. We propose that this selfish behavior of TEs paradoxically could make them diverse and less harmful to the host.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.248
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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